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Introduction
The global production system is used to produce the daily forecasts of greenhouse gases, i.e. carbon dioxide (CO2) and methane (CH4) across the globe. Satellite observations of atmospheric composition are merged with a detailed computer simulation of the atmosphere using a method called data assimilation. The resulting analyses, i.e. maps of atmospheric composition, are used as initial conditions for the daily forecasts of atmospheric composition of long-lived greenhouse gases (i.e. CO2 and CH4). Analyses and forecasts for greenhouse gases are produced once a day. The analysis has a resolution of approximately 25km and it is produced 4 days behind real-time due to latency of satellite retrievals. The high-resolution forecast is run separately a few hours behind real time, with initial conditions based on a 4-day forecast of the analysis experiment, and it is also run at higher resolution (~9km). The lower cost of the CO₂ and CH₄ makes it possible to produce the analyses and forecasts at a higher resolution than the CAMS forecast of reactive gases and aerosols (~40km).
The IFS model and data assimilation system configurations for greenhouse gases
The model used in the CAMS Global greenhouse gas forecasts is the Integrated Forecasting System (IFS) that also produces ECMWF weather forecasts, but with additional modules of greenhouse gases that have been developed within CAMS and precursor projects GEMS and MACC. The IFS model documentation for various model cycles can be found on https://www.ecmwf.int/en/forecasts/documentation-and-support/changes-ecmwf-model/ifs-documentation. Please note that the IFS cycle changes during the years, and this page documents the current operational cycle 48r1 (IFS Documentation CY48R1 - Part VIII: Atmospheric Composition). The main components of the IFS model and data assimilation system for CO2 and CH4 are listed below (see References and IFS documentation link provided above for further details).
Model
The following processes of atmospheric composition are considered in the IFS model:
- transport of greenhouse gases
- uptake and release of CO2 by vegetation and release of CO2 from soil over the land modelled by the ECLand surface model (Boussetta et al., 2021, Agusti-Panareda et al., 2014) based on the Farquhar photosynthesis model implementation by Yin and Struik (2009) with a Biogenic Flux Adjustment Scheme (BFAS, Agusti-Panareda et al., 2016)
The high-resolution forecast also includes carbon monoxide (CO) as a tracer with a simplified linear CO scheme based on Claeyman et al. (2010). It is initialised each day from the operational CAMS atmospheric composition analysis, which includes reactive species and aerosols (Implementation of IFS cycle 48r1 for CAMS#DocumentVersionsDocumentversions).
Prescribed emissions and surface fluxes
- Anthropogenic emissions from the CAMS-GLOB-ANT inventory with CAMS-TEMPO seasonal cycle (available from the ADS)
- Injection and diurnal cycle of emissions
- CO2 fluxes over ocean from Jena CarboScope (v2020, Rödenbeck et al. 2013).
- CH4 wetland fluxes from a climatology of the LPJ-HYMN dataset from Spahni et al. (2011).
- Biomass burning emissions inferred from satellite observations of fire activity using IS4FIRES injection heights (GFAS v1.4)
- Other CH4 emissions from oceans (Lambert and Schmidt, 1993), soil sink (Ridgwell et al., 1999), termites (Sanderson, 1996) and wild animals (Houweling et al., 1999).
- Climatology of CH4 chemical loss rate in the atmosphere from Bergamaschi et al. (2009).
Data assimilation system
The IFS uses a four-dimensional variational data assimilation method (4D-VAR) for the assimilation of a wide range of meteorological observations as well as satellite retrievals of atmospheric composition. The GHG analysis configuration has been documented by Massart et al. (2014, 2016) and Agusti-Panareda et al. (2023).
Observations assimilated
Satellite observations are used by CAMS to constrain the global forecast model, ensuring the forecasts are as accurate as possible. The process of merging the numerical forecast model with the observations is called data assimilation. CAMS produces global services in two modes: real-time chemistry and aerosol and real-time long-lived greenhouse gases.
The CAMS GHG production system uses currently satellite (GOSAT/TANSO, METOP-C/IASI) retrievals in 4D-Var data assimilation system to constrain the initial atmospheric state. Two categories of observations listed below (Table 71) are provided in near real-time (2-4 days behind). Column-averaged concentration retrievals of CO2 (XCO2) and XCH4 (CH4) using TANSO/GOSAT measurements are provided by the University of Bremen (UB) and SRON, respectively. The "Laboratoire de Météorologie Dynamique" (LMD) provides the mid-tropospheric columns of CH4 (MT-CH4) and of CO2 (MT-CO2) using both IASI/METOP and AMSU measurements.
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Evolution of the CAMS global greenhouse gases system
Implementation date | Cycle | Summary of changes | Resolution/Resolution change | New species | ||||||||||
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27 February 2024 | 48r1 |
| Horizontal: 9 km, Vertical: 137 levels |
Data access
Users with direct access to MARS can browse the data on the MARS catalogue.
Data organisation in MARS
CAMS Global greenhouse gases forecasts | CAMS Global greenhouse gases analysis | |
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Stream | oper | oper |
expver | 0001 | 0011 |
class | gg | gg |
Type |
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Levtype |
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Spatial grid
CAMS Global greenhouse gases forecast and analysis have resolution of approximately 9 km (around 0.10 degrees for regular lat/lon grid) and 25 km (approximately 0.25 degrees), respectively. The data are archived either as spectral coefficients with a triangular truncation of Tco1279 or on a reduced Gaussian grid with a resolution of O1280 for the GHG forecast and Tco399 (O400) for the GHG analysis.
Temporal frequency
The CAMS Global greenhouse gases 5-day forecasts run daily from 00 UTC and the data are available every 3 hours. The analyses are available every 6 hours at 00 UTC, 06 UTC, 12 UTC and 18 UTC.
Data format
Model level fields are in GRIB2 format. All other fields are in GRIB1, unless otherwise indicated.
Level listings
Pressure levels: 1000/950/925/900/850/800/700/600/500/400/300/250/200/150/100/70/50/30/20/10/7/5/3/2/1
Model levels: 1/to/137, which are described at L137 model level definitions.
Parameter listings
Table
12: Single level parameters (last reviewed on )
Name | Units | Shortname | Param ID | fc | an | Note |
CO2 column-mean molar fraction | ppm | tcco2 | 210064 | x | x | This variable is also known as XCO2 |
CH4 column-mean molar fraction | ppb | tcch4 | 210065 | x | x | This variable is also known as XCH4 |
Total column Carbon monoxide | kg m-2 | tcco | 210127 | x | ||
Accumulated Carbon Dioxide Net Ecosystem Exchange (NEE) | kg m-2 | aco2nee | 228080 | x | NEE is computed as a sum of GPP and Reco (see rows below). This flux has been corrected with the Biogenic Adjustment Scheme. NEE = corrected GPP + corrected Reco | |
Flux of Carbon Dioxide Net Ecosystem Exchange (NEE) | kg m-2 s-1 | fco2nee | 228083 | x | NEE is computed as a sum of GPP and Reco (see rows below). This flux has been corrected with the Biogenic Adjustment Scheme. NEE = corrected GPP + corrected Reco | |
Accumulated Carbon Dioxide Gross Primary Production (GPP) | kg m-2 | aco2gpp | 228081 | x | Uncorrected flux. For corrected flux, apply the scaling factor gppbfas to GPP: Corrected GPP = Uncorrected GPP * gppbfas | |
Flux of Carbon Dioxide Gross Primary Production (GPP) | kg m-2 s-1 | fco2gpp | 228084 | x | Uncorrected flux. For corrected flux, apply the scaling factor gppbfas to GPP: Corrected GPP = Uncorrected GPP* gppbfas | |
Accumulated Carbon Dioxide Ecosystem Respiration (Reco) | kg m-2 | aco2rec | 228082 | x | Uncorrected flux. For corrected flux, apply the scaling factor recbfas to Reco: Corrected Reco = Uncorrected Reco * recbfas | |
Flux of Carbon Dioxide Ecosystem Respiration (Reco) | kg m-2 s-1 | fco2rec | 228085 | x | Uncorrected flux. For corrected flux, apply the scaling factor recbfas to Reco: Corrected Reco = Uncorrected Reco * recbfas | |
GPP coefficient from Biogenic Flux Adjustment System | dimensionless | gppbfas | 228078 | x | Scaling factor for GPP in Biogenic Flux Adjustment Scheme (BFAS) scheme (Agusti-Panareda et al., 2016 for further details) | |
Rec coefficient from Biogenic Flux Adjustment System | dimensionless | recbfas | 228079 | x | Scaling factor for GPP in Biogenic Flux Adjustment Scheme (BFAS) scheme (see Agusti-Panareda et al., 2016 for further details) | |
2m dewpoint temperature | K | 2d | 168 | X | X | |
2m temperature | K | 2t | 167 | X | X | |
10m u-component of wind | m s-1 | 10u | 165 | X | X | |
10m v-component of wind | m s-1 | 10v | 166 | X | X | |
10m wind gust in the last 3 hours | m s-1 | 10fg3 | 228028 | X | X | |
Boundary layer height | m | blh | 159 | X | ||
Cloud base height | m | cbh | 228023 | X | ||
Convective available potential energy | J kg-1 | cape | 59 | X | ||
Convective inhibition | J kg-1 | cin | 228001 | X | X | |
Convective precipitation | m | cp | 143 | X | X | |
Evaporation | m of water equivalent | e | 182 | X | ||
Forecast albedo | (0 - 1) | fal | 243 | X | ||
Friction velocity | m s-1 | zust | 228003 | X | ||
Height of convective cloud top | m | hcct | 228046 | X | ||
High cloud cover | (0 - 1) | hcc | 188 | X | X | |
Lake cover | (0 - 1) | cl | 26 | X | X | |
Large-scale precipitation | m | lsp | 142 | X | ||
Leaf area index, high vegetation | m2 m-2 | lai_hv | 67 | X | X | |
Leaf area index, low vegetation | m2 m-2 | lai_lv | 66 | X | X | |
Low cloud cover | (0 - 1) | lcc | 186 | X | X | |
Medium cloud cover | (0 - 1) | mcc | 187 | X | X | |
Potential evaporation | m | pev | 228251 | X | ||
Precipitation type | code table (4.201) | ptype | 260015 | X | ||
Sea surface temperature | ||||||
Sea-ice cover | (0 - 1) | ci | 31 | X | X | |
Skin reservoir content | m of water equivalent | |||||
Skin temperature | K | skt | 235 | X | X | |
Snow albedo | (0 - 1) | asn | 32 | X | X | |
Snow depth | m of water equivalent | sd | 141 | X | X | |
Sunshine duration | s | sund | 189 | X | ||
Surface latent heat flux | J m-2 | slhf | 147 | X | ||
Surface net solar radiation | J m-2 | ssr | 176 | X | ||
Surface net solar radiation, clear sky | J m-2 | ssrc | 210 | X | ||
Surface net thermal radiation | J m-2 | str | 177 | X | ||
Surface net thermal radiation, clear sky | J m-2 | strc | 211 | X | ||
Surface sensible heat flux | J m-2 | sshf | 146 | X | X | |
Surface solar radiation downward clear-sky | J m-2 | ssrdc | 228129 | X | ||
Surface solar radiation downwards | J m-2 | ssrd | 169 | X | ||
Surface thermal radiation downward clear-sky | J m-2 | strdc | 228130 | X | ||
Surface thermal radiation downwards | J m-2 | strd | 175 | X | ||
Total column cloud ice water | kg m-2 | tciw | 79 | X | X | |
Total column cloud liquid water | kg m-2 | tclw | 78 | X | X | |
Total column rain water | kg m-2 | tcrw | 228089 | X | X | |
Total column snow water | kg m-2 | tcsw | 228090 | X | X | |
Total column supercooled liquid water | kg m-2 | tcslw | 228088 | X | ||
Total column water | kg m-2 | tcw | 136 | X | X | |
Total precipitation | m | tp | 228 | X | ||
Total sky direct solar radiation at surface | J m-2 | fdir | 228021 | X | ||
Vertically integrated moisture divergence | kg m-2 | vimd | 213 | X | ||
Visibility | m | vis | 3020 | X |
Table
23: Multi
level parameterslevel parameters (last reviewed
onon )
Name | Units | Shortname | Param ID | fc | an | Note |
Carbon dioxide mass mixing ratio | kg kg-1 | co2 | 210061 | x | x | |
Methane | kg kg-1 | ch4 | 210062 | x | x | |
Carbon monoxide mass mixing ratio | kg kg-1 | co | 210123 | x | ||
Fraction of cloud cover | (0 - 1) | cc | 248 | X | X | |
Geopotential | m2 s-2 | z | 129 | X | X | |
Logarithm of surface pressure | Numeric | lnsp | 152 | X | X | |
Potential vorticity | K m2 kg-1 s-1 | pv | 60 | X | X | |
Relative humidity | % | r | 157 | X | X | |
Specific cloud ice water content | kg kg-1 | ciwc | 247 | X | X | |
Specific cloud liquid water content | kg kg-1 | clwc | 246 | X | X | |
Specific humidity | ||||||
Specific rain water content | kg kg-1 | crwc | 75 | X | X | |
Specific snow water content | kg kg-1 | cswc | 76 | X | X | |
Temperature | K | t | 130 | X | X | |
U-component of wind | m s-1 | u | 131 | X | X | |
V-component of wind | m s-1 | v | 132 | X | X | |
Vertical velocity | Pa s-1 | w | 135 | X | X |
Evaluation and Quality Assurance reports
The global forecasting system is continually being evaluated to ensure the output meets the expected requirements. Comprehensive Evaluation and Quality Assurance (EQA) reports are provided on a quarterly basis. Before each upgrade of the global forecasting system, the new system is tested and evaluated, and a so-called "e-suite EQA report" is produced.
Quality monitoring graphics
CAMS uses a multitude of independent data sets to routinely monitor its global forecasts. It works with various data providers, acquiring the observations with appropriate timeliness and generating graphics that show the differences between the forecasts and the independent observations. See at https://atmosphere.copernicus.eu/charts/packages/cams_monitoring/
Every day, CAMS provides also charts of the five-day forecasts of greenhouse gases here.
Guidelines
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- Agustí-Panareda, A., Massart, S., Chevallier, F., Balsamo, G., Boussetta, S., Dutra, E., and Beljaars, A., 2016: A biogenic CO2 flux adjustment scheme for the mitigation of large-scale biases in global atmospheric CO2 analyses and forecasts, Atmos. Chem. Phys., 16, 10399–10418, https://doi.org/10.5194/acp-16-10399-2016
- Agustí-Panareda, A., Massart, S., Chevallier, F., Boussetta, S., Balsamo, G., Beljaars, A., Ciais, P., Deutscher, N. M., Engelen, R., Jones, L., Kivi, R., Paris, J.-D., Peuch, V.-H., Sherlock, V., Vermeulen, A. T., Wennberg, P. O., and Wunch, D., 2014: Forecasting global atmospheric CO2, Atmos. Chem. Phys., 14, 11959–11983, https://doi.org/10.5194/acp-14-11959-2014
- Boussetta, S.; Balsamo, G.; Arduini, G.; Dutra, E.; McNorton, J.; Choulga, M.; Agustí-Panareda, A.; Beljaars, A.; Wedi, N.; Munõz-Sabater, J.; et al. ECLand: The ECMWF Land Surface Modelling System. Atmosphere2021, 12, 723. https://doi.org/10.3390/atmos12060723
- Barré, J., Aben, I., Agustí-Panareda, A., Balsamo, G., Bousserez, N., Dueben, P., Engelen, R., Inness, A., Lorente, A., McNorton, J., Peuch, V.-H., Radnoti, G., and Ribas, R.: Systematic detection of local CH4emissions anomalies combining satellite measurements and high-resolution forecasts, Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2020-550, in review, 2020.
- Bergamaschi, P. et al. Inverse modeling of global and regional CH4 emissions using SCIAMACHY satellite retrievals. J. Geophys. Res.114, D22301 (2009).
- Bozzo, A. and Benedetti, A. and Flemming, J. and Kipling, Z. and Rémy, S., 2020: An aerosol climatology for global models based on the tropospheric aerosol scheme in the Integrated Forecasting System of ECMWF, Geosci. Model Dev., 3, 1007–1034, https://doi.org/10.5194/gmd-13-1007-2020
- Claeyman, M. et al. A linear CO chemistry parameterization in a chemistry-transport model: Evaluation and application to data assimilation. Atmospheric Chem. Phys.10, 6097–6115 (2010).Diamantakis, M. and , 2017: A positive definite tracer mass fixer for high resolution weather and atmospheric composition forecasts, ECMWF Technical Memoranda, No. 819, 2017.
- Flemming, J., Huijnen, V., Arteta, J., Bechtold, P., Beljaars, A., Blechschmidt, A.-M., Diamantakis, M., Engelen, R. J., Gaudel, A., Inness, A., Jones, L., Josse, B., Katragkou, E., Marecal, V., Peuch, V.-H., Richter, A., Schultz, M. G., Stein, O., and Tsikerdekis, A., 2015: Tropospheric chemistry in the Integrated Forecasting System of ECMWF, Geosci. Model Dev., 8, 975–1003, https://doi.org/10.5194/gmd-8-975-2015
- Flemming, J. and Benedetti, A. and Inness, A. and Engelen, R. J. and Jones, L. and Huijnen, V. and Remy, S. and Parrington, M. and Suttie, M. and Bozzo, A. and Peuch, V.-H. and Akritidis, D. and Katragkou, E., 2017: The CAMS interim Reanalysis of Carbon Monoxide, Ozone and Aerosol for 2003–2015, Atmos. Chem. Phys.,17, 1945-1983, https://doi.org/10.5194/acp-17-1945-2017
- Houweling, S., Kaminski, T., Dentener, F., Lelieveld, J. & Heimann, M. Inverse modeling of methane sources and sinks using the adjoint of a global transport model. J. Geophys. Res. Atmospheres104, 26137–26160 (1999).
- Huijnen, V. and Flemming, J. and Chabrillat, S. and Errera, Q. and Christophe, Y. and Blechschmidt, A.-M. and Richter, A. and Eskes, H., 2016: C-IFS-CB05-BASCOE: stratospheric chemistry in the Integrated Forecasting System of ECMWF, Geosci. Model Dev., 9, 3071–3091, https://doi.org/10.5194/gmd-9-3071-2016
- Inness, A., Ades, M., Agustí-Panareda, A., Barré, J., Benedictow, A., Blechschmidt, A.-M., Dominguez, J. J., Engelen, R., Eskes, H., Flemming, J., Huijnen, V., Jones, L., Kipling, Z., Massart, S., Parrington, M., Peuch, V.-H., Razinger, M., Remy, S., Schulz, M., and Suttie, M., 2019: The CAMS reanalysis of atmospheric composition, Atmos. Chem. Phys., 19, 3515–3556, https://doi.org/10.5194/acp-19-3515-2019
- Inness, A. and Flemming, J. and Heue, K.-P. and Lerot, C. and Loyola, D. and Ribas, R. and Valks, P. and van Roozendael, M. and Xu, J. and Zimmer, W., 2019: Monitoring and assimilation tests with TROPOMI data in the CAMS system:
near-real-time total column ozone, Atmos. Chem. Phys., 19, 3939–3962, https://acp.copernicus.org/articles/19/3939/2019/ - Inness, A. and Coauthors, 2015: Data assimilation of satellite-retrieved ozone, carbon monoxide and nitrogen dioxide with ECMWF's Composition-IFS, Atmos. Chem. Phys., 15, 5275-5303, https://doi.org/10.5194/acp-15-5275-2015
- Lambert, G. R. & Schmidt, S. Reevaluation of the oceanic flux of methane: uncertainties and long term variations. Chemosphere26, 579–589 (1993).
- Massart, S., Agusti-Panareda, A., Aben, I., Butz, A., Chevallier, F., Crevoisier, C., Engelen, R., Frankenberg, C., and Hasekamp, O., 2014: Assimilation of atmospheric methane products into the MACC-II system: from SCIAMACHY to TANSO and IASI, Atmos. Chem. Phys., 14, 6139–6158, https://doi.org/10.5194/acp-14-6139-2014
- Massart, S., Agustí-Panareda, A., Heymann, J., Buchwitz, M., Chevallier, F., Reuter, M., Hilker, M., Burrows, J. P., Deutscher, N. M., Feist, D. G., Hase, F., Sussmann, R., Desmet, F., Dubey, M. K., Griffith, D. W. T., Kivi, R., Petri, C., Schneider, M., and Velazco, V. A., 2016: Ability of the 4-D-Var analysis of the GOSAT BESD XCO2 retrievals to characterize atmospheric CO2 at large and synoptic scales, Atmos. Chem. Phys., 16, 1653–1671, https://doi.org/10.5194/acp-16-1653-2016
- Rémy, S. and Kipling, Z. and Flemming, J. and Boucher, O. and Nabat, P. and Michou, M. and Bozzo, A. and Ades, M. and Huijnen, V. and Benedetti, A. and Engelen, R. and Peuch, V.-H. and Morcrette, J.-J., 2019: Description and evaluation of the tropospheric aerosol scheme in the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecasting System (IFS-AER, cycle 45R1), Geosci. Model Dev., 12, 4627-4659, https://doi.org/10.5194/gmd-12-4627-2019
- Ridgwell, A. J., Marshall, S. J. & Gregson, K. Consumption of atmospheric methane by soils: A process-based model. Glob. Biogeochem. Cycles13, 59–70 (1999).
- Rödenbeck, C. et al. Global surface-ocean pCO2 and sea-Air CO2 flux variability from an observation-driven ocean mixed-layer scheme. Ocean Sci.9, 193–216 (2013).
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X. Yin, P.C. Struik, C3 and C4 photosynthesis models: An overview from the perspective of crop modelling/ NJAS - Wageningen Journal of Life Sciences 57 (2009) 27–38
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This document has been produced in the context of the Copernicus Atmosphere Monitoring Service (CAMS). The activities leading to these results have been contracted by the European Centre for Medium-Range Weather Forecasts, operator of CAMS on behalf of the European Union (Delegation Agreement signed on 11/11/2014 and Contribution Agreement signed on 22/07/2021). All information in this document is provided "as is" and no guarantee or warranty is given that the information is fit for any particular purpose. The users thereof use the information at their sole risk and liability. For the avoidance of all doubt , the European Commission and the European Centre for Medium - Range Weather Forecasts have no liability in respect of this document, which is merely representing the author's view. |
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